Time Series Forecasting with Irregular Intervals in Applied Behavior Analysis

Multivariate Time Series Forecasting needs Cross Variable Loss

This work proposes CvLoss, a plug-in structural regularizer that constrains forecast residuals on a cross-variable graph and shows that CvLoss consistently improves competitive forecasting models, outperforms representative learning objectives, and is compatible with a variety of forecasting backbones.

Adaptive Imputation for Daily Meteorological Time Series Under Bimodal Seasonal Regimes

Daily meteorological time series data constitute a critical scientific infrastructure for climate monitoring, trend analysis, and decision-making in many industrial fields, particularly in applied meteorology. This study proposes and validates an adaptive imputation approach for daily meteorological time series under bimodal seasonality, aimed at selecting a reconstruction method based on the size of the gap and the local climate structure. This approach was organized into three phases. In the first phase, an exploratory characterization of the missing data was conducted, yielding 539 observed gaps, which led to the evaluation of 80 synthetic scenarios defined by two-time windows, four variables, and five length categories. The second phase involved constructing a controlled benchmark that included 10 local and global reconstruction methods evaluated using a multiplicative score (“Climate Score”). In this phase, the results showed a clear pattern of non-dominance: Prophet outperformed in micro-gaps, while seasonal jitter and seasonal KNN dominated in small to very large gaps. In the third phase, the final imputation showed high preservation of the global statistics, with standard deviation ratios ranging from 0.9898 to 0.9971 and very low KS statistics for five of the six variables. These results demonstrate that imputation should not be applied using a single method for all cases; instead, the characteristics of the data must be considered. This concept aligns with data-centric AI, where it is essential to understand and preserve the statistical and seasonal structure of the series.

Neural Time Series Forecasting: A Problem-Driven Survey from Data Challenges to Model Choice

The literature is organized around five recurring difficulties: nonlinear and nonstationary behavior, contamination and structural breaks, uncertainty, long contexts and cross-variable dependence, and limited target-domain data.

Second-order season-trend GRU for long time series forecasting

Iterative ensemble forecasting with residual integration for complex and volatile demand time series

Accurate forecasting in volatile and structurally complex demand environments requires models capable of capturing evolving trends, seasonality, nonlinear behavior and externally driven fluctuations. This study aims to propose the iterative ensemble forecasting with residual integration (IEFRI) framework, a structured ensemble methodology that improves forecast accuracy by explicitly and iteratively learning from residual errors rather than treating them as random noise. Unlike conventional one-stage ensemble approaches, IEFRI uses a multistage refinement process in which forecasts from heterogeneous base models are progressively corrected through successive neural and regression-based residual-learning cycles, allowing both nonlinear and linear error structures to be captured systematically. The framework integrates Holt–Winters exponential smoothing, seasonal auto regressive integrated moving average (SARIMA), artificial neural networks (ANNs) and multiple linear regression (MLR) within a decomposition-driven architecture. IEFRI is empirically validated using 25 years of monthly demand data (April 1998–March 2024) covering all major petroleum products in India – petrol, diesel, Liquified Petroleum Gas (LPG), jet fuel and kerosene – each exhibiting strong seasonality, trend shifts, structural change and sensitivity to macroeconomic and policy factors. Comparative evaluation using standard accuracy measures shows that IEFRI consistently outperforms individual models and widely used ensemble techniques such as bagging, boosting, random forests and stacked regression. While demonstrated using petroleum demand, the framework is generalizable to other complex and uncertainty-prone time-series forecasting applications. This study develops and evaluates the IEFRI framework for complex demand time series. The approach combines heterogeneous base forecasting models – Holt–Winters exponential smoothing, SARIMA, ANNs and MLR – within a decomposition-driven ensemble structure. Initial forecasts are aggregated and subsequently refined through a multistage residual-learning process that integrates neural-network-based nonlinear correction and iterative regression-based linear adjustment. Model performance is assessed using rolling-origin out-of-sample evaluation and standard accuracy measures mean absolute percentatge error (MAPE), symmetric mean absolute percentage error (SMAPE), root mean square error (RMSE). The framework is empirically validated using 25 years of monthly demand data across five petroleum products, enabling robust comparison with conventional ensemble methods. The empirical results show that the proposed IEFRI framework consistently improves forecast accuracy across all five petroleum products – petrol, diesel, LPG, jet fuel and kerosene – each characterized by strong seasonality, structural change and nonlinear dynamics. Compared with individual base models and conventional ensemble techniques such as bagging, boosting, random forests and stacked regression, IEFRI achieves lower forecasting errors under rolling-origin evaluation. The iterative residual-learning mechanism is found to be effective in systematically reducing both nonlinear and linear forecast errors across successive refinement stages. Performance gains are stable across products with differing demand characteristics, demonstrating the robustness of the framework. These findings indicate that explicitly modeling and reintegrating residual information enhances ensemble forecasting accuracy in complex, volatile demand environments. Despite its strengths, this study has several limitations. First, the analysis relies on secondary data compiled from multiple sources, requiring interpolation and harmonization, which may introduce minor measurement bias. Second, the IEFRI framework is validated using historical monthly data from a single domain, and its generalizability to other sectors and higher-frequency data has not been empirically tested. Third, while the iterative residual-learning process improves accuracy, it increases computational complexity and may be sensitive to data quality and parameter settings. Finally, the study does not incorporate probabilistic or uncertainty forecasting, which could further enhance decision support in practice. The proposed IEFRI framework provides practitioners with a systematic and robust approach for improving forecast accuracy in environments characterized by volatility, nonlinearity and structural change. By explicitly learning from residual errors, the model supports more reliable demand forecasts that can enhance operational planning, capacity allocation, inventory management and medium-term strategic decision-making. The framework is flexible and can be integrated with existing forecasting systems that already use statistical or machine learning models, allowing organizations to improve performance without fully replacing current tools. For policymakers and managers, more accurate forecasts enable better resource planning, risk mitigation and policy evaluation in sectors where demand uncertainty has significant economic and operational consequences. This study is based on original research and contributes original value by introducing the IEFRI framework, which advances ensemble forecasting beyond conventional one-stage aggregation. Unlike standard ensemble methods that treat residuals as random noise, IEFRI explicitly and iteratively models residual errors through a structured combination of neural-network-based nonlinear learning and regression-based linear correction. This multistage residual integration provides a systematic mechanism for continuous forecast refinement. The study has been subjected to extensive empirical validation using the demand for various petroleum products which exhibit multiple, structurally diverse demand series. The framework offers a reusable, domain-agnostic methodology for improving forecast accuracy in complex, volatile time-series environments.

TimeSage-EV: A Live Benchmark for Agentic Time Series Analysis in Evolving Environments

Time series analysis in high-stakes domains relies on recurring data releases, where new observations can alter the evidence base and the validity of later conclusions. Existing time series QA benchmarks mostly rely on fixed snapshots, leaving temporal validity and cutoff-aware evidence use unevaluated. We introduce TimeSage-EV, a live benchmark for agentic time series analysis in evolving environments. It tracks 60 real institutional scenarios across 6 domains, comprising 1,485 scenario-period QA pairs from Feb 2023 to May 2026 and spanning monthly, weekly, daily, and irregular release cadences. At each period, large language model (LLM) agents receive time series data and source reports, while the withheld target release provides ground truth. TimeSage-EV evaluates state identification, data summarization, and outlook reasoning. Experiments with frontier LLM agents and TimeSage-1.0, a novel self-evolving agent with a reusable analytical skill library, reveal significant performance gaps across model tiers and recurring failures in temporal validity, exogenous context use, and adaptation. We release TimeSage-EV as a research resource with monthly updates, code, a leaderboard, and failure-mode analyses.